{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.1 Class Activation Map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tensorflow Walkthrough"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "from tensorflow.python.ops import nn_ops, gen_nn_ops\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "from models.models_4_1 import MNIST_CNN\n",
    "from utils import find_roi\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "mnist_cluttered = np.load('./MNIST_cluttered/mnist_sequence1_sample_5distortions5x5.npz')\n",
    "X_train = mnist_cluttered['X_train']\n",
    "y_train = mnist_cluttered['y_train']\n",
    "X_valid = mnist_cluttered['X_valid']\n",
    "y_valid = mnist_cluttered['y_valid']\n",
    "X_test = mnist_cluttered['X_test']\n",
    "y_test = mnist_cluttered['y_test']\n",
    "\n",
    "logdir = './tf_logs/4_1_CAM/'\n",
    "ckptdir = logdir + 'model'\n",
    "\n",
    "if not os.path.exists(logdir):\n",
    "    os.mkdir(logdir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Building Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.name_scope('Classifier'):\n",
    "\n",
    "    # Initialize neural network\n",
    "    DNN = MNIST_CNN('CNN')\n",
    "\n",
    "    # Setup training process\n",
    "    X = tf.placeholder(tf.float32, [None, 1600], name='X')\n",
    "    Y = tf.placeholder(tf.int64, [None], name='Y')\n",
    "    Y_hot = tf.one_hot(Y, 10)\n",
    "\n",
    "    activations, logits = DNN(X)\n",
    "    \n",
    "    tf.add_to_collection('CAM', X)\n",
    "    \n",
    "    for activation in activations:\n",
    "        tf.add_to_collection('CAM', activation)\n",
    "\n",
    "    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y_hot))\n",
    "\n",
    "    optimizer = tf.train.AdamOptimizer().minimize(cost, var_list=DNN.vars)\n",
    "\n",
    "    correct_prediction = tf.equal(tf.argmax(logits, 1), Y)\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "\n",
    "cost_summary = tf.summary.scalar('Cost', cost)\n",
    "accuray_summary = tf.summary.scalar('Accuracy', accuracy)\n",
    "summary = tf.summary.merge_all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Training Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 cost = 2.033042523 accuracy = 0.242500000\n",
      "Epoch: 0002 cost = 1.390657289 accuracy = 0.534400000\n",
      "Epoch: 0003 cost = 1.008045424 accuracy = 0.682800002\n",
      "Epoch: 0004 cost = 0.793261112 accuracy = 0.747399999\n",
      "Epoch: 0005 cost = 0.669011008 accuracy = 0.789900001\n",
      "Epoch: 0006 cost = 0.583145978 accuracy = 0.816799999\n",
      "Epoch: 0007 cost = 0.521201497 accuracy = 0.835999998\n",
      "Epoch: 0008 cost = 0.473216166 accuracy = 0.851299996\n",
      "Epoch: 0009 cost = 0.434501152 accuracy = 0.865799996\n",
      "Epoch: 0010 cost = 0.402649050 accuracy = 0.875399997\n",
      "Epoch: 0011 cost = 0.375527744 accuracy = 0.886000000\n",
      "Epoch: 0012 cost = 0.351153830 accuracy = 0.894500001\n",
      "Epoch: 0013 cost = 0.329674218 accuracy = 0.901000003\n",
      "Epoch: 0014 cost = 0.310661063 accuracy = 0.906300002\n",
      "Epoch: 0015 cost = 0.293649128 accuracy = 0.911300003\n",
      "Epoch: 0016 cost = 0.278376295 accuracy = 0.916400003\n",
      "Epoch: 0017 cost = 0.265304229 accuracy = 0.919800004\n",
      "Epoch: 0018 cost = 0.253397216 accuracy = 0.922400003\n",
      "Epoch: 0019 cost = 0.242828627 accuracy = 0.925800005\n",
      "Epoch: 0020 cost = 0.233107253 accuracy = 0.930300004\n",
      "Accuracy: 0.916\n"
     ]
    }
   ],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "saver = tf.train.Saver()\n",
    "file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())\n",
    "\n",
    "# Hyper parameters\n",
    "training_epochs = 20\n",
    "batch_size = 100\n",
    "\n",
    "for epoch in range(training_epochs):\n",
    "    total_batch = int(np.shape(X_train)[0] / batch_size)\n",
    "    avg_cost = 0\n",
    "    avg_acc = 0\n",
    "    \n",
    "    for i in range(total_batch):\n",
    "        batch_xs, batch_ys = X_train[i * batch_size:(i+1) * batch_size], y_train[i * batch_size:(i+1) * batch_size].reshape([-1])\n",
    "        _, c, a, summary_str = sess.run([optimizer, cost, accuracy, summary], feed_dict={X: batch_xs, Y: batch_ys})\n",
    "        avg_cost += c / total_batch\n",
    "        avg_acc += a / total_batch\n",
    "        \n",
    "        file_writer.add_summary(summary_str, epoch * total_batch + i)\n",
    "\n",
    "    print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost), 'accuracy =', '{:.9f}'.format(avg_acc))\n",
    "    \n",
    "    saver.save(sess, ckptdir)\n",
    "\n",
    "print('Accuracy:', sess.run(accuracy, feed_dict={X: X_test, Y: y_test.reshape([-1])}))\n",
    "\n",
    "sess.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Restoring Subgraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./tf_logs/2_6_CAM/model\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "sess = tf.InteractiveSession()\n",
    "\n",
    "new_saver = tf.train.import_meta_graph(ckptdir + '.meta')\n",
    "new_saver.restore(sess, tf.train.latest_checkpoint(logdir))\n",
    "\n",
    "activations = tf.get_collection('CAM')\n",
    "weights = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='.*kernel.*')\n",
    "\n",
    "X = activations[0]\n",
    "activations = activations[1:]\n",
    "\n",
    "sample_imgs = [X_train[y_train.reshape([-1]) == i][1] for i in range(10)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Generating CAMs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cams = [tf.reduce_sum(activations[5] * weights[-1][:,i], axis=3, keep_dims=True) for i in range(10)]\n",
    "resized_cams = [tf.image.resize_bilinear(cams[i], [40,40], align_corners=True) for i in range(10)]\n",
    "\n",
    "hmaps = np.reshape([sess.run(resized_cams[i], feed_dict={X: sample_imgs[i][None]}) for i in range(10)], [10, 40, 40])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Displaying Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fwHULrUSDEZGEORx1n13t3RAREZE1oqy+hbt/EwC7jevqxaxHgxGRhDkMrtu0REREpCSp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\nERFZFf8fgnjB8oF+dRAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28a43c89ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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W6QXiS21nLAi/l5a8kJoNg3XbNqR2aiH51pyu2Y7uB6a3mbFaza6r07aprsUz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\nCOAREXlAVZ/IYBMTnDYwRLR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      "text/plain": [
       "<matplotlib.figure.Figure at 0x28a521f9208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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M7EZJ/1jSeybbJul7JH1w0uV9kn5oi14GAABoiDqxRZPwKBfQEB2ZZqfzKNev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\nAACw4/5/YBJ1eBy9QaQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28a5a9d0e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EBKiwuq2cAQAAqq1usQUTE6AiGmaabvAnCQAARqNusUV9RgpsBDVaOQMAANRA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\nAC4Z0ft0cJ7Jeo/PzU0Jzlk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      "text/plain": [
       "<matplotlib.figure.Figure at 0x28a5d131f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EHgJwNoBrALw46XYLgG8CeG8/x+KVEqKCiFWW/MmoCmBARKoABtH9MHkJgC8kv78FwC/k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\nkxIiIiIiIlpTnJQQEREREdGa+v+rXHbfdDEhQAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28a621f2eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    plt.figure(figsize=(15,15))\n",
    "    for j in range(2):\n",
    "        plt.subplot(5, 2, i * 2 + j + 1)\n",
    "        plt.imshow(sample_imgs[i * 2 + j].reshape(40,40), cmap='gray')\n",
    "        plt.imshow(hmaps[i * 2 + j], alpha=0.8)\n",
    "        plt.title('Digit {} CAM'.format(i * 2 + j))\n",
    "        plt.colorbar()\n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
